Upon completion of this course, participants will be able to:

• Apply **data cleansing and transformation techniques** to aggregate and standardize disparate data reports from diverse NBFI sub-sectors (e.g., microfinance, factoring).
• Utilize **statistical analysis** (e.g., regression, trend analysis) to benchmark NBFI performance against peer groups and industry averages.
• Develop and implement automated **Key Risk Indicator (KRI)** dashboards and early warning scorecards for continuous off-site surveillance.
• Master the use of **data visualization tools** (e.g., Tableau, Power BI) to communicate complex financial and risk patterns effectively to senior management.
• Employ **segmentation techniques** (e.g., clustering) to group NBFIs by risk profile, business model, and geographic exposure for targeted supervision.
• Design a comprehensive framework for identifying data quality issues and establishing rigorous data governance for NBFI reporting.
• Apply **geo-spatial visualization** techniques to analyze the geographic concentration of lending and potential systemic risk in specific regions.
• Formulate data-driven hypotheses for on-site examination based on anomalies identified through visualization and analysis.

• Financial Supervisors and Analysts focused on Non-Bank Financial Institutions (NBFIs).
• Off-Site Surveillance and Early Warning System Teams.
• Data Scientists and Business Intelligence Specialists in Regulatory Authorities.
• Risk Managers and Financial Reporting Analysts at NBFIs.
• Internal Audit and Compliance Monitoring Teams.

• Hands-on Workshops with Data Visualization Tools (e.g., Tableau, Power BI) and Statistical Software
• Group Activities on Designing a Continuous Off-Site Surveillance Dashboard for an NBFI Peer Group
• Case Studies on Early Detection of NBFI Stress Using Data Visualization
• Expert Lectures on Statistical Segmentation and Benchmarking Techniques
• Workshops on Data Cleansing and Anomaly Detection in Financial Reports
• Individual Exercises on Developing a Supervisory Risk Hypothesis from a Data Dashboard

Unit 1: The NBFI Data Challenge and Foundations

• Characteristics of NBFI data: lack of standardization, high diversity of business models, reporting frequency challenges.
• Defining the scope of supervisory data requirements (prudential, conduct, systemic) for the non-bank sector.
• Introduction to the data analysis lifecycle: collection, cleansing, transformation, analysis, and visualization.
• Review of essential data tools and software for regulatory surveillance (e.g., SQL, R/Python, BI Tools).
• The critical role of data quality and validation in generating reliable supervisory intelligence.

Unit 2: Data Cleansing and Statistical Analysis

• Techniques for **data cleansing, anomaly detection, and outlier identification** in NBFI financial reports.
• Methodologies for data aggregation and standardization to enable **peer group analysis** across the sector.
• Application of basic statistical analysis (mean, standard deviation, correlation) to identify unusual risk trends.
• Using **early warning scorecards** and heat maps derived from Key Risk Indicators (KRIs).
• Segmentation of the NBFI sector using clustering or factor analysis based on risk and balance sheet composition.

Unit 3: Advanced Visualization for Supervisory Reporting

• Principles of effective data visualization: clarity, accuracy, and minimizing distortion.
• Mastering **dashboard design** for continuous off-site surveillance (e.g., monitoring liquidity, asset quality, leverage).
• Using specialized visualization types: network graphs for interconnectedness, geo-spatial maps for concentration risk.
• Protocols for linking interactive dashboards to the underlying data source for drill-down and verification.
• Techniques for communicating complex statistical findings and risk ratings clearly to senior policymakers.

Unit 4: Early Warning Systems and Anomaly Detection

• Developing and backtesting **early warning models** for NBFI failure or significant prudential breach.
• Using automated change detection and trend analysis to flag unusual shifts in NBFI behavior or portfolio composition.
• Protocols for setting appropriate thresholds and minimizing false positive alerts in the surveillance system.
• Formulating data-driven hypotheses and key questions for the on-site examination team based on surveillance findings.
• The role of external data (e.g., market sentiment, economic indicators) in enriching NBFI surveillance.

Unit 5: Data Governance and Future Trends

• Establishing a robust **data governance framework** for all supervisory data used for analysis and reporting.
• Compliance requirements for data privacy and security in the handling of sensitive institutional data.
• Auditing the analytical models and visualization tools for accuracy and consistency (model validation).
• Future trends: integrating AI/ML output into supervisory dashboards for predictive risk analysis.
• Strategic planning for enhancing the mandatory reporting templates for NBFIs to improve data quality.